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Smoke Object Segmentation and the Dynamic Growth Feature Model for Video-Based Smoke Detection Systems

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School of Computer Science and Engineering, University of Aizu, Fukushima 965-0006, Japan
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College of Aeronautics and Engineering, Kent State University, Kent, OH 44242, USA
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Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 874-8577, Bangladesh
*
Authors to whom correspondence should be addressed.
Symmetry 2020, 12(7), 1075; https://doi.org/10.3390/sym12071075 (registering DOI)
Received: 7 June 2020 / Revised: 25 June 2020 / Accepted: 29 June 2020 / Published: 30 June 2020
This article concerns smoke detection in the early stages of a fire. Using the computer-aided system, the efficient and early detection of smoke may stop a massive fire incident. Without considering the multiple moving objects on background and smoke particles analysis (i.e., pattern recognition), smoke detection models show suboptimal performance. To address this, this paper proposes a hybrid smoke segmentation and an efficient symmetrical simulation model of dynamic smoke to extract a smoke growth feature based on temporal frames from a video. In this model, smoke is segmented from the multi-moving object on the complex background using the Gaussian’s Mixture Model (GMM) and HSV (hue-saturation-value) color segmentation to encounter the candidate smoke and non-smoke regions in the preprocessing stage. The preprocessed temporal frames with moving smoke are analyzed by the dynamic smoke growth analysis and spatial-temporal frame energy feature extraction model. In dynamic smoke growth analysis, the temporal frames are segmented in blocks and the smoke growth representations are formulated from corresponding blocks. Finally, the classifier was trained using the extracted features to classify and detect smoke using a Radial Basis Function (RBF) non-linear Gaussian kernel-based binary Support Vector Machine (SVM). For validating the proposed smoke detection model, multi-conditional video clips are used. The experimental results suggest that the proposed model outperforms state-of-the-art algorithms. View Full-Text
Keywords: smoke detection; pattern recognition; background subtraction; Gaussian’s mixture model (GMM); HSV color segmentation; smoke growth features; support vector machine (SVM) smoke detection; pattern recognition; background subtraction; Gaussian’s mixture model (GMM); HSV color segmentation; smoke growth features; support vector machine (SVM)
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Islam, M.R.; Amiruzzaman, M.; Nasim, S.; Shin, J. Smoke Object Segmentation and the Dynamic Growth Feature Model for Video-Based Smoke Detection Systems. Symmetry 2020, 12, 1075.

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